Data Scientist
Develops and validates LLM/ML models for healthcare chart abstraction from unstructured/structured data, evaluates cutting-edge NLP/AI techniques for clinical problems, and collaborates with customers to optimize workflows. Requires 5-7+ years data science experience including 3-4 years in healthcare.
Responsibilities
- Develop, validate, and measure the impact of large language model (LLM) and other ML-based approaches for chart abstraction from both unstructured and structured healthcare data.
- Apply and rigorously evaluate cutting-edge methods to solve real clinical problems.
- Work hand-in-hand with our customers to best leverage Layer Health’s platform and supercharge their workflows.
- Stay up-to-date on the latest in applied NLP and generative AI techniques and proactively explore these technologies where applicable.
- Establish and enforce best practices for data science and analytics.
- Cultivate and foster a robust and thoughtful data science and product culture that drives the company forward.
Requirements
- 5-7+ years of professional experience as a data scientist working for a company with major ML dependencies.
- At least 3-4 years of healthcare / clinical applications experience.
- A solid foundation of ML/methods.
- Fluent programming skills in Python, and fluency with modern data science and ML/NLP libraries (PyTorch, Tensorflow, HuggingFace, etc.).
- Deep fluency and instincts for data manipulation, treatment, and evaluation, with the ability to wrangle large, complex datasets efficiently and methodically.
- Familiarity with modern applied LLM techniques and their practical implementations (any experience using these techniques is a bonus).
- Proactive mindset to identify and solve problems, continuously improving our data science capabilities.
- A strong communicator who thrives in a customer-focused, fast-paced environment.
- An excited and adaptable team player who wants to disrupt the healthcare industry with AI/ML.
Compensation
Expected compensation range: $180,000 - $200,000. Compensation is dependent on experience, overall fit to our role, and candidate location.
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